// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H
#define EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H

namespace Eigen {

/** \class TensorEvaluator
  * \ingroup CXX11_Tensor_Module
  *
  * \brief The tensor evaluator classes.
  *
  * These classes are responsible for the evaluation of the tensor expression.
  *
  * TODO: add support for more types of expressions, in particular expressions
  * leading to lvalues (slicing, reshaping, etc...)
  */

// Generic evaluator
template <typename Derived, typename Device> struct TensorEvaluator
{
    typedef typename Derived::Index Index;
    typedef typename Derived::Scalar Scalar;
    typedef typename Derived::Scalar CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    typedef typename Derived::Dimensions Dimensions;
    typedef Derived XprType;
    static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
    typedef typename internal::traits<Derived>::template MakePointer<Scalar>::Type TensorPointerType;
    typedef StorageMemory<Scalar, Device> Storage;
    typedef typename Storage::Type EvaluatorPointerType;

    // NumDimensions is -1 for variable dim tensors
    static const int NumCoords = internal::traits<Derived>::NumDimensions > 0 ? internal::traits<Derived>::NumDimensions : 0;

    enum
    {
        IsAligned = Derived::IsAligned,
        PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
        BlockAccess = internal::is_arithmetic<typename internal::remove_const<Scalar>::type>::value,
        PreferBlockAccess = false,
        Layout = Derived::Layout,
        CoordAccess = NumCoords > 0,
        RawAccess = true
    };

    typedef typename internal::remove_const<Scalar>::type ScalarNoConst;

    //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
    typedef internal::TensorBlockDescriptor<NumCoords, Index> TensorBlockDesc;
    typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;

    typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumCoords, Layout, Index> TensorBlock;
    //===--------------------------------------------------------------------===//

    EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device)
        : m_data(device.get((const_cast<TensorPointerType>(m.data())))), m_dims(m.dimensions()), m_device(device)
    {
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; }

    EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType dest)
    {
        if (!NumTraits<typename internal::remove_const<Scalar>::type>::RequireInitialization && dest)
        {
            m_device.memcpy((void*)(m_device.get(dest)), m_device.get(m_data), m_dims.TotalSize() * sizeof(Scalar));
            return false;
        }
        return true;
    }

#ifdef EIGEN_USE_THREADS
    template <typename EvalSubExprsCallback> EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType dest, EvalSubExprsCallback done)
    {
        // TODO(ezhulenev): ThreadPoolDevice memcpy is blockign operation.
        done(evalSubExprsIfNeeded(dest));
    }
#endif  // EIGEN_USE_THREADS

    EIGEN_STRONG_INLINE void cleanup() {}

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
    {
        eigen_assert(m_data != NULL);
        return m_data[index];
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
    {
        eigen_assert(m_data != NULL);
        return m_data[index];
    }

    template <int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
    {
        return internal::ploadt<PacketReturnType, LoadMode>(m_data + index);
    }

    // Return a packet starting at `index` where `umask` specifies which elements
    // have to be loaded. Type/size of mask depends on PacketReturnType, e.g. for
    // Packet16f, `umask` is of type uint16_t and if a bit is 1, corresponding
    // float element will be loaded, otherwise 0 will be loaded.
    // Function has been templatized to enable Sfinae.
    template <typename PacketReturnTypeT>
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
        typename internal::enable_if<internal::unpacket_traits<PacketReturnTypeT>::masked_load_available, PacketReturnTypeT>::type
        partialPacket(Index index, typename internal::unpacket_traits<PacketReturnTypeT>::mask_t umask) const
    {
        return internal::ploadu<PacketReturnTypeT>(m_data + index, umask);
    }

    template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x)
    {
        return internal::pstoret<Scalar, PacketReturnType, StoreMode>(m_data + index, x);
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const
    {
        eigen_assert(m_data != NULL);
        if (static_cast<int>(Layout) == static_cast<int>(ColMajor))
        {
            return m_data[m_dims.IndexOfColMajor(coords)];
        }
        else
        {
            return m_data[m_dims.IndexOfRowMajor(coords)];
        }
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(const array<DenseIndex, NumCoords>& coords)
    {
        eigen_assert(m_data != NULL);
        if (static_cast<int>(Layout) == static_cast<int>(ColMajor))
        {
            return m_data[m_dims.IndexOfColMajor(coords)];
        }
        else
        {
            return m_data[m_dims.IndexOfRowMajor(coords)];
        }
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
    {
        return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketType<CoeffReturnType, Device>::size);
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const
    {
        return internal::TensorBlockResourceRequirements::any();
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch, bool /*root_of_expr_ast*/ = false) const
    {
        assert(m_data != NULL);
        return TensorBlock::materialize(m_data, m_dims, desc, scratch);
    }

    template <typename TensorBlock> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(const TensorBlockDesc& desc, const TensorBlock& block)
    {
        assert(m_data != NULL);

        typedef typename TensorBlock::XprType TensorBlockExpr;
        typedef internal::TensorBlockAssignment<Scalar, NumCoords, TensorBlockExpr, Index> TensorBlockAssign;

        TensorBlockAssign::Run(TensorBlockAssign::target(desc.dimensions(), internal::strides<Layout>(m_dims), m_data, desc.offset()), block.expr());
    }

    EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_data; }

#ifdef EIGEN_USE_SYCL
    // binding placeholder accessors to a command group handler for SYCL
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const { m_data.bind(cgh); }
#endif
protected:
    EvaluatorPointerType m_data;
    Dimensions m_dims;
    const Device EIGEN_DEVICE_REF m_device;
};

namespace {
    template <typename T> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T loadConstant(const T* address) { return *address; }
// Use the texture cache on CUDA devices whenever possible
#if defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 350
    template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float loadConstant(const float* address) { return __ldg(address); }
    template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double loadConstant(const double* address) { return __ldg(address); }
    template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Eigen::half loadConstant(const Eigen::half* address)
    {
        return Eigen::half(half_impl::raw_uint16_to_half(__ldg(&address->x)));
    }
#endif
#ifdef EIGEN_USE_SYCL
    // overload of load constant should be implemented here based on range access
    template <cl::sycl::access::mode AcMd, typename T> T& loadConstant(const Eigen::TensorSycl::internal::RangeAccess<AcMd, T>& address) { return *address; }
#endif
}  // namespace

// Default evaluator for rvalues
template <typename Derived, typename Device> struct TensorEvaluator<const Derived, Device>
{
    typedef typename Derived::Index Index;
    typedef typename Derived::Scalar Scalar;
    typedef typename Derived::Scalar CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    typedef typename Derived::Dimensions Dimensions;
    typedef const Derived XprType;
    typedef typename internal::traits<Derived>::template MakePointer<const Scalar>::Type TensorPointerType;
    typedef StorageMemory<const Scalar, Device> Storage;
    typedef typename Storage::Type EvaluatorPointerType;

    typedef typename internal::remove_const<Scalar>::type ScalarNoConst;

    // NumDimensions is -1 for variable dim tensors
    static const int NumCoords = internal::traits<Derived>::NumDimensions > 0 ? internal::traits<Derived>::NumDimensions : 0;
    static const int PacketSize = PacketType<CoeffReturnType, Device>::size;

    enum
    {
        IsAligned = Derived::IsAligned,
        PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
        BlockAccess = internal::is_arithmetic<ScalarNoConst>::value,
        PreferBlockAccess = false,
        Layout = Derived::Layout,
        CoordAccess = NumCoords > 0,
        RawAccess = true
    };

    //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
    typedef internal::TensorBlockDescriptor<NumCoords, Index> TensorBlockDesc;
    typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;

    typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumCoords, Layout, Index> TensorBlock;
    //===--------------------------------------------------------------------===//

    EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device) : m_data(device.get(m.data())), m_dims(m.dimensions()), m_device(device) {}

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; }

    EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data)
    {
        if (!NumTraits<typename internal::remove_const<Scalar>::type>::RequireInitialization && data)
        {
            m_device.memcpy((void*)(m_device.get(data)), m_device.get(m_data), m_dims.TotalSize() * sizeof(Scalar));
            return false;
        }
        return true;
    }

#ifdef EIGEN_USE_THREADS
    template <typename EvalSubExprsCallback> EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType dest, EvalSubExprsCallback done)
    {
        // TODO(ezhulenev): ThreadPoolDevice memcpy is a blockign operation.
        done(evalSubExprsIfNeeded(dest));
    }
#endif  // EIGEN_USE_THREADS

    EIGEN_STRONG_INLINE void cleanup() {}

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
    {
        eigen_assert(m_data != NULL);
        return loadConstant(m_data + index);
    }

    template <int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
    {
        return internal::ploadt_ro<PacketReturnType, LoadMode>(m_data + index);
    }

    // Return a packet starting at `index` where `umask` specifies which elements
    // have to be loaded. Type/size of mask depends on PacketReturnType, e.g. for
    // Packet16f, `umask` is of type uint16_t and if a bit is 1, corresponding
    // float element will be loaded, otherwise 0 will be loaded.
    // Function has been templatized to enable Sfinae.
    template <typename PacketReturnTypeT>
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
        typename internal::enable_if<internal::unpacket_traits<PacketReturnTypeT>::masked_load_available, PacketReturnTypeT>::type
        partialPacket(Index index, typename internal::unpacket_traits<PacketReturnTypeT>::mask_t umask) const
    {
        return internal::ploadu<PacketReturnTypeT>(m_data + index, umask);
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const
    {
        eigen_assert(m_data != NULL);
        const Index index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_dims.IndexOfColMajor(coords) : m_dims.IndexOfRowMajor(coords);
        return loadConstant(m_data + index);
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
    {
        return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketType<CoeffReturnType, Device>::size);
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const
    {
        return internal::TensorBlockResourceRequirements::any();
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch, bool /*root_of_expr_ast*/ = false) const
    {
        assert(m_data != NULL);
        return TensorBlock::materialize(m_data, m_dims, desc, scratch);
    }

    EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_data; }
#ifdef EIGEN_USE_SYCL
    // binding placeholder accessors to a command group handler for SYCL
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const { m_data.bind(cgh); }
#endif
protected:
    EvaluatorPointerType m_data;
    Dimensions m_dims;
    const Device EIGEN_DEVICE_REF m_device;
};

// -------------------- CwiseNullaryOp --------------------

template <typename NullaryOp, typename ArgType, typename Device> struct TensorEvaluator<const TensorCwiseNullaryOp<NullaryOp, ArgType>, Device>
{
    typedef TensorCwiseNullaryOp<NullaryOp, ArgType> XprType;

    TensorEvaluator(const XprType& op, const Device& device) : m_functor(op.functor()), m_argImpl(op.nestedExpression(), device), m_wrapper() {}

    typedef typename XprType::Index Index;
    typedef typename XprType::Scalar Scalar;
    typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
    typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
    typedef StorageMemory<CoeffReturnType, Device> Storage;
    typedef typename Storage::Type EvaluatorPointerType;

    enum
    {
        IsAligned = true,
        PacketAccess = internal::functor_traits<NullaryOp>::PacketAccess
#ifdef EIGEN_USE_SYCL
                       && (PacketType<CoeffReturnType, Device>::size > 1)
#endif
            ,
        BlockAccess = false,
        PreferBlockAccess = false,
        Layout = TensorEvaluator<ArgType, Device>::Layout,
        CoordAccess = false,  // to be implemented
        RawAccess = false
    };

    //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
    typedef internal::TensorBlockNotImplemented TensorBlock;
    //===--------------------------------------------------------------------===//

    EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); }

    EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) { return true; }

#ifdef EIGEN_USE_THREADS
    template <typename EvalSubExprsCallback> EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) { done(true); }
#endif  // EIGEN_USE_THREADS

    EIGEN_STRONG_INLINE void cleanup() {}

    EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const { return m_wrapper(m_functor, index); }

    template <int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
    {
        return m_wrapper.template packetOp<PacketReturnType, Index>(m_functor, index);
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
    {
        return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketType<CoeffReturnType, Device>::size);
    }

    EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }

#ifdef EIGEN_USE_SYCL
    // binding placeholder accessors to a command group handler for SYCL
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const { m_argImpl.bind(cgh); }
#endif

private:
    const NullaryOp m_functor;
    TensorEvaluator<ArgType, Device> m_argImpl;
    const internal::nullary_wrapper<CoeffReturnType, NullaryOp> m_wrapper;
};

// -------------------- CwiseUnaryOp --------------------

template <typename UnaryOp, typename ArgType, typename Device> struct TensorEvaluator<const TensorCwiseUnaryOp<UnaryOp, ArgType>, Device>
{
    typedef TensorCwiseUnaryOp<UnaryOp, ArgType> XprType;

    enum
    {
        IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
        PacketAccess = int(TensorEvaluator<ArgType, Device>::PacketAccess) & int(internal::functor_traits<UnaryOp>::PacketAccess),
        BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess,
        PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
        Layout = TensorEvaluator<ArgType, Device>::Layout,
        CoordAccess = false,  // to be implemented
        RawAccess = false
    };

    TensorEvaluator(const XprType& op, const Device& device) : m_device(device), m_functor(op.functor()), m_argImpl(op.nestedExpression(), device) {}

    typedef typename XprType::Index Index;
    typedef typename XprType::Scalar Scalar;
    typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
    typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
    typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
    typedef StorageMemory<CoeffReturnType, Device> Storage;
    typedef typename Storage::Type EvaluatorPointerType;
    static const int NumDims = internal::array_size<Dimensions>::value;

    //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
    typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
    typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;

    typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock ArgTensorBlock;

    typedef internal::TensorCwiseUnaryBlock<UnaryOp, ArgTensorBlock> TensorBlock;
    //===--------------------------------------------------------------------===//

    EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); }

    EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType)
    {
        m_argImpl.evalSubExprsIfNeeded(NULL);
        return true;
    }

#ifdef EIGEN_USE_THREADS
    template <typename EvalSubExprsCallback> EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done)
    {
        m_argImpl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
    }
#endif  // EIGEN_USE_THREADS

    EIGEN_STRONG_INLINE void cleanup() { m_argImpl.cleanup(); }

    EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const { return m_functor(m_argImpl.coeff(index)); }

    template <int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
    {
        return m_functor.packetOp(m_argImpl.template packet<LoadMode>(index));
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
    {
        const double functor_cost = internal::functor_traits<UnaryOp>::Cost;
        return m_argImpl.costPerCoeff(vectorized) + TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const
    {
        static const double functor_cost = internal::functor_traits<UnaryOp>::Cost;
        return m_argImpl.getResourceRequirements().addCostPerCoeff({0, 0, functor_cost / PacketSize});
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch, bool /*root_of_expr_ast*/ = false) const
    {
        return TensorBlock(m_argImpl.block(desc, scratch), m_functor);
    }

    EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }

#ifdef EIGEN_USE_SYCL
    // binding placeholder accessors to a command group handler for SYCL
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const { m_argImpl.bind(cgh); }
#endif

private:
    const Device EIGEN_DEVICE_REF m_device;
    const UnaryOp m_functor;
    TensorEvaluator<ArgType, Device> m_argImpl;
};

// -------------------- CwiseBinaryOp --------------------

template <typename BinaryOp, typename LeftArgType, typename RightArgType, typename Device>
struct TensorEvaluator<const TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType>, Device>
{
    typedef TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType> XprType;

    enum
    {
        IsAligned = int(TensorEvaluator<LeftArgType, Device>::IsAligned) & int(TensorEvaluator<RightArgType, Device>::IsAligned),
        PacketAccess = int(TensorEvaluator<LeftArgType, Device>::PacketAccess) & int(TensorEvaluator<RightArgType, Device>::PacketAccess) &
                       int(internal::functor_traits<BinaryOp>::PacketAccess),
        BlockAccess = int(TensorEvaluator<LeftArgType, Device>::BlockAccess) & int(TensorEvaluator<RightArgType, Device>::BlockAccess),
        PreferBlockAccess = int(TensorEvaluator<LeftArgType, Device>::PreferBlockAccess) | int(TensorEvaluator<RightArgType, Device>::PreferBlockAccess),
        Layout = TensorEvaluator<LeftArgType, Device>::Layout,
        CoordAccess = false,  // to be implemented
        RawAccess = false
    };

    TensorEvaluator(const XprType& op, const Device& device)
        : m_device(device), m_functor(op.functor()), m_leftImpl(op.lhsExpression(), device), m_rightImpl(op.rhsExpression(), device)
    {
        EIGEN_STATIC_ASSERT(
            (static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) ||
             internal::traits<XprType>::NumDimensions <= 1),
            YOU_MADE_A_PROGRAMMING_MISTAKE);
        eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions()));
    }

    typedef typename XprType::Index Index;
    typedef typename XprType::Scalar Scalar;
    typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
    typedef typename TensorEvaluator<LeftArgType, Device>::Dimensions Dimensions;
    typedef StorageMemory<CoeffReturnType, Device> Storage;
    typedef typename Storage::Type EvaluatorPointerType;

    static const int NumDims = internal::array_size<typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value;

    //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
    typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
    typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;

    typedef typename TensorEvaluator<const LeftArgType, Device>::TensorBlock LeftTensorBlock;
    typedef typename TensorEvaluator<const RightArgType, Device>::TensorBlock RightTensorBlock;

    typedef internal::TensorCwiseBinaryBlock<BinaryOp, LeftTensorBlock, RightTensorBlock> TensorBlock;
    //===--------------------------------------------------------------------===//

    EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
    {
        // TODO: use right impl instead if right impl dimensions are known at compile time.
        return m_leftImpl.dimensions();
    }

    EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType)
    {
        m_leftImpl.evalSubExprsIfNeeded(NULL);
        m_rightImpl.evalSubExprsIfNeeded(NULL);
        return true;
    }

#ifdef EIGEN_USE_THREADS
    template <typename EvalSubExprsCallback> EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done)
    {
        // TODO(ezhulenev): Evaluate two expression in parallel?
        m_leftImpl.evalSubExprsIfNeededAsync(nullptr, [this, done](bool) { m_rightImpl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); }); });
    }
#endif  // EIGEN_USE_THREADS

    EIGEN_STRONG_INLINE void cleanup()
    {
        m_leftImpl.cleanup();
        m_rightImpl.cleanup();
    }

    EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const { return m_functor(m_leftImpl.coeff(index), m_rightImpl.coeff(index)); }
    template <int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
    {
        return m_functor.packetOp(m_leftImpl.template packet<LoadMode>(index), m_rightImpl.template packet<LoadMode>(index));
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
    {
        const double functor_cost = internal::functor_traits<BinaryOp>::Cost;
        return m_leftImpl.costPerCoeff(vectorized) + m_rightImpl.costPerCoeff(vectorized) + TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const
    {
        static const double functor_cost = internal::functor_traits<BinaryOp>::Cost;
        return internal::TensorBlockResourceRequirements::merge(m_leftImpl.getResourceRequirements(), m_rightImpl.getResourceRequirements())
            .addCostPerCoeff({0, 0, functor_cost / PacketSize});
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch, bool /*root_of_expr_ast*/ = false) const
    {
        desc.DropDestinationBuffer();
        return TensorBlock(m_leftImpl.block(desc, scratch), m_rightImpl.block(desc, scratch), m_functor);
    }

    EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }

#ifdef EIGEN_USE_SYCL
    // binding placeholder accessors to a command group handler for SYCL
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const
    {
        m_leftImpl.bind(cgh);
        m_rightImpl.bind(cgh);
    }
#endif
private:
    const Device EIGEN_DEVICE_REF m_device;
    const BinaryOp m_functor;
    TensorEvaluator<LeftArgType, Device> m_leftImpl;
    TensorEvaluator<RightArgType, Device> m_rightImpl;
};

// -------------------- CwiseTernaryOp --------------------

template <typename TernaryOp, typename Arg1Type, typename Arg2Type, typename Arg3Type, typename Device>
struct TensorEvaluator<const TensorCwiseTernaryOp<TernaryOp, Arg1Type, Arg2Type, Arg3Type>, Device>
{
    typedef TensorCwiseTernaryOp<TernaryOp, Arg1Type, Arg2Type, Arg3Type> XprType;

    enum
    {
        IsAligned = TensorEvaluator<Arg1Type, Device>::IsAligned & TensorEvaluator<Arg2Type, Device>::IsAligned & TensorEvaluator<Arg3Type, Device>::IsAligned,
        PacketAccess = TensorEvaluator<Arg1Type, Device>::PacketAccess && TensorEvaluator<Arg2Type, Device>::PacketAccess &&
                       TensorEvaluator<Arg3Type, Device>::PacketAccess && internal::functor_traits<TernaryOp>::PacketAccess,
        BlockAccess = false,
        PreferBlockAccess = TensorEvaluator<Arg1Type, Device>::PreferBlockAccess || TensorEvaluator<Arg2Type, Device>::PreferBlockAccess ||
                            TensorEvaluator<Arg3Type, Device>::PreferBlockAccess,
        Layout = TensorEvaluator<Arg1Type, Device>::Layout,
        CoordAccess = false,  // to be implemented
        RawAccess = false
    };

    TensorEvaluator(const XprType& op, const Device& device)
        : m_functor(op.functor()), m_arg1Impl(op.arg1Expression(), device), m_arg2Impl(op.arg2Expression(), device), m_arg3Impl(op.arg3Expression(), device)
    {
        EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<Arg1Type, Device>::Layout) == static_cast<int>(TensorEvaluator<Arg3Type, Device>::Layout) ||
                             internal::traits<XprType>::NumDimensions <= 1),
                            YOU_MADE_A_PROGRAMMING_MISTAKE);

        EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind, typename internal::traits<Arg2Type>::StorageKind>::value),
                            STORAGE_KIND_MUST_MATCH)
        EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind, typename internal::traits<Arg3Type>::StorageKind>::value),
                            STORAGE_KIND_MUST_MATCH)
        EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::Index, typename internal::traits<Arg2Type>::Index>::value),
                            STORAGE_INDEX_MUST_MATCH)
        EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::Index, typename internal::traits<Arg3Type>::Index>::value),
                            STORAGE_INDEX_MUST_MATCH)

        eigen_assert(dimensions_match(m_arg1Impl.dimensions(), m_arg2Impl.dimensions()) && dimensions_match(m_arg1Impl.dimensions(), m_arg3Impl.dimensions()));
    }

    typedef typename XprType::Index Index;
    typedef typename XprType::Scalar Scalar;
    typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
    typedef typename TensorEvaluator<Arg1Type, Device>::Dimensions Dimensions;
    typedef StorageMemory<CoeffReturnType, Device> Storage;
    typedef typename Storage::Type EvaluatorPointerType;

    //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
    typedef internal::TensorBlockNotImplemented TensorBlock;
    //===--------------------------------------------------------------------===//

    EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
    {
        // TODO: use arg2 or arg3 dimensions if they are known at compile time.
        return m_arg1Impl.dimensions();
    }

    EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType)
    {
        m_arg1Impl.evalSubExprsIfNeeded(NULL);
        m_arg2Impl.evalSubExprsIfNeeded(NULL);
        m_arg3Impl.evalSubExprsIfNeeded(NULL);
        return true;
    }
    EIGEN_STRONG_INLINE void cleanup()
    {
        m_arg1Impl.cleanup();
        m_arg2Impl.cleanup();
        m_arg3Impl.cleanup();
    }

    EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const { return m_functor(m_arg1Impl.coeff(index), m_arg2Impl.coeff(index), m_arg3Impl.coeff(index)); }
    template <int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
    {
        return m_functor.packetOp(
            m_arg1Impl.template packet<LoadMode>(index), m_arg2Impl.template packet<LoadMode>(index), m_arg3Impl.template packet<LoadMode>(index));
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
    {
        const double functor_cost = internal::functor_traits<TernaryOp>::Cost;
        return m_arg1Impl.costPerCoeff(vectorized) + m_arg2Impl.costPerCoeff(vectorized) + m_arg3Impl.costPerCoeff(vectorized) +
               TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
    }

    EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }

#ifdef EIGEN_USE_SYCL
    // binding placeholder accessors to a command group handler for SYCL
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const
    {
        m_arg1Impl.bind(cgh);
        m_arg2Impl.bind(cgh);
        m_arg3Impl.bind(cgh);
    }
#endif

private:
    const TernaryOp m_functor;
    TensorEvaluator<Arg1Type, Device> m_arg1Impl;
    TensorEvaluator<Arg2Type, Device> m_arg2Impl;
    TensorEvaluator<Arg3Type, Device> m_arg3Impl;
};

// -------------------- SelectOp --------------------

template <typename IfArgType, typename ThenArgType, typename ElseArgType, typename Device>
struct TensorEvaluator<const TensorSelectOp<IfArgType, ThenArgType, ElseArgType>, Device>
{
    typedef TensorSelectOp<IfArgType, ThenArgType, ElseArgType> XprType;
    typedef typename XprType::Scalar Scalar;

    enum
    {
        IsAligned = TensorEvaluator<ThenArgType, Device>::IsAligned & TensorEvaluator<ElseArgType, Device>::IsAligned,
        PacketAccess =
            TensorEvaluator<ThenArgType, Device>::PacketAccess & TensorEvaluator<ElseArgType, Device>::PacketAccess & PacketType<Scalar, Device>::HasBlend,
        BlockAccess = TensorEvaluator<IfArgType, Device>::BlockAccess && TensorEvaluator<ThenArgType, Device>::BlockAccess &&
                      TensorEvaluator<ElseArgType, Device>::BlockAccess,
        PreferBlockAccess = TensorEvaluator<IfArgType, Device>::PreferBlockAccess || TensorEvaluator<ThenArgType, Device>::PreferBlockAccess ||
                            TensorEvaluator<ElseArgType, Device>::PreferBlockAccess,
        Layout = TensorEvaluator<IfArgType, Device>::Layout,
        CoordAccess = false,  // to be implemented
        RawAccess = false
    };

    TensorEvaluator(const XprType& op, const Device& device)
        : m_condImpl(op.ifExpression(), device), m_thenImpl(op.thenExpression(), device), m_elseImpl(op.elseExpression(), device)
    {
        EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<ThenArgType, Device>::Layout)),
                            YOU_MADE_A_PROGRAMMING_MISTAKE);
        EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<ElseArgType, Device>::Layout)),
                            YOU_MADE_A_PROGRAMMING_MISTAKE);
        eigen_assert(dimensions_match(m_condImpl.dimensions(), m_thenImpl.dimensions()));
        eigen_assert(dimensions_match(m_thenImpl.dimensions(), m_elseImpl.dimensions()));
    }

    typedef typename XprType::Index Index;
    typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
    typedef typename TensorEvaluator<IfArgType, Device>::Dimensions Dimensions;
    typedef StorageMemory<CoeffReturnType, Device> Storage;
    typedef typename Storage::Type EvaluatorPointerType;

    static const int NumDims = internal::array_size<Dimensions>::value;

    //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
    typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
    typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;

    typedef typename TensorEvaluator<const IfArgType, Device>::TensorBlock IfArgTensorBlock;
    typedef typename TensorEvaluator<const ThenArgType, Device>::TensorBlock ThenArgTensorBlock;
    typedef typename TensorEvaluator<const ElseArgType, Device>::TensorBlock ElseArgTensorBlock;

    struct TensorSelectOpBlockFactory
    {
        template <typename IfArgXprType, typename ThenArgXprType, typename ElseArgXprType> struct XprType
        {
            typedef TensorSelectOp<const IfArgXprType, const ThenArgXprType, const ElseArgXprType> type;
        };

        template <typename IfArgXprType, typename ThenArgXprType, typename ElseArgXprType>
        typename XprType<IfArgXprType, ThenArgXprType, ElseArgXprType>::type
        expr(const IfArgXprType& if_expr, const ThenArgXprType& then_expr, const ElseArgXprType& else_expr) const
        {
            return typename XprType<IfArgXprType, ThenArgXprType, ElseArgXprType>::type(if_expr, then_expr, else_expr);
        }
    };

    typedef internal::TensorTernaryExprBlock<TensorSelectOpBlockFactory, IfArgTensorBlock, ThenArgTensorBlock, ElseArgTensorBlock> TensorBlock;
    //===--------------------------------------------------------------------===//

    EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
    {
        // TODO: use then or else impl instead if they happen to be known at compile time.
        return m_condImpl.dimensions();
    }

    EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType)
    {
        m_condImpl.evalSubExprsIfNeeded(NULL);
        m_thenImpl.evalSubExprsIfNeeded(NULL);
        m_elseImpl.evalSubExprsIfNeeded(NULL);
        return true;
    }

#ifdef EIGEN_USE_THREADS
    template <typename EvalSubExprsCallback> EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done)
    {
        m_condImpl.evalSubExprsIfNeeded(nullptr, [this, done](bool) {
            m_thenImpl.evalSubExprsIfNeeded(nullptr, [this, done](bool) { m_elseImpl.evalSubExprsIfNeeded(nullptr, [done](bool) { done(true); }); });
        });
    }
#endif  // EIGEN_USE_THREADS

    EIGEN_STRONG_INLINE void cleanup()
    {
        m_condImpl.cleanup();
        m_thenImpl.cleanup();
        m_elseImpl.cleanup();
    }

    EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const { return m_condImpl.coeff(index) ? m_thenImpl.coeff(index) : m_elseImpl.coeff(index); }
    template <int LoadMode> EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
    {
        internal::Selector<PacketSize> select;
        EIGEN_UNROLL_LOOP
        for (Index i = 0; i < PacketSize; ++i) { select.select[i] = m_condImpl.coeff(index + i); }
        return internal::pblend(select, m_thenImpl.template packet<LoadMode>(index), m_elseImpl.template packet<LoadMode>(index));
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
    {
        return m_condImpl.costPerCoeff(vectorized) + m_thenImpl.costPerCoeff(vectorized).cwiseMax(m_elseImpl.costPerCoeff(vectorized));
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const
    {
        auto then_req = m_thenImpl.getResourceRequirements();
        auto else_req = m_elseImpl.getResourceRequirements();

        auto merged_req = internal::TensorBlockResourceRequirements::merge(then_req, else_req);
        merged_req.cost_per_coeff = then_req.cost_per_coeff.cwiseMax(else_req.cost_per_coeff);

        return internal::TensorBlockResourceRequirements::merge(m_condImpl.getResourceRequirements(), merged_req);
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch, bool /*root_of_expr_ast*/ = false) const
    {
        // It's unsafe to pass destination buffer to underlying expressions, because
        // output might be aliased with one of the inputs.
        desc.DropDestinationBuffer();

        return TensorBlock(m_condImpl.block(desc, scratch), m_thenImpl.block(desc, scratch), m_elseImpl.block(desc, scratch), TensorSelectOpBlockFactory());
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return NULL; }

#ifdef EIGEN_USE_SYCL
    // binding placeholder accessors to a command group handler for SYCL
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const
    {
        m_condImpl.bind(cgh);
        m_thenImpl.bind(cgh);
        m_elseImpl.bind(cgh);
    }
#endif
private:
    TensorEvaluator<IfArgType, Device> m_condImpl;
    TensorEvaluator<ThenArgType, Device> m_thenImpl;
    TensorEvaluator<ElseArgType, Device> m_elseImpl;
};

}  // end namespace Eigen

#endif  // EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H
